package Analysis;

public class PearsonCorrelationCalculatorClass extends CorrelationCalculatorAbstractClass{
	
	public double calculateCorrelation(double[] scores1,double[] scores2)
	{
		return calculatePearsonCorrelation(scores1, scores2);
	}
	
	public static double calculatePearsonCorrelation(double[] scores1,double[] scores2)
	{
		double result = 0;
		double sum_sq_x = 0;
		double sum_sq_y = 0;
		double sum_coproduct = 0;
		
		if(scores1==null || scores2==null)
			return Double.NaN;		
		
		double mean_x = scores1[0];
		double mean_y = scores2[0];
		for(int i=2;i<scores1.length+1;i+=1){
			double sweep =Double.valueOf(i-1)/i;
			double delta_x = scores1[i-1]-mean_x;
			double delta_y = scores2[i-1]-mean_y;
			sum_sq_x += delta_x * delta_x * sweep;
			sum_sq_y += delta_y * delta_y * sweep;
			sum_coproduct += delta_x * delta_y * sweep;
			mean_x += delta_x / i;
			mean_y += delta_y / i;
		}
		double pop_sd_x = (double) Math.sqrt(sum_sq_x/scores1.length);
		double pop_sd_y = (double) Math.sqrt(sum_sq_y/scores1.length);
		double cov_x_y = sum_coproduct / scores1.length;
		result = cov_x_y / (pop_sd_x*pop_sd_y);
		return result;
	}
}
